So today I'm talking Doctor Jimeng Sun from right here at Georgia Tech.
Jimeng joined us earlier this year after many years at IBM, where his
work has depended heavily on analyzing data from electronic health records.
Jimeng thanks very much for taking the time to be in the course.
I've heard you talk many times about the difficulties of working with EHR data.
Could you explain some of those to the students?
>> Sure.
First, Mark great to be here.
So electronic health records very important for my research and
many other people who work on clinical informatics,
house care informatics, also need to access electronic house records.
But electronic house record is very messy, and lot of missing data in there,
because they are collected for billing and clinical operation purpose.
They're not designed for research.
So there are a lot of information in the HR systems, but for a given
patient they only have a small subset of those, and they're not uniform.
So some patient have a lot more information than the others, and
that makes the electronic health records very messy, very difficult to deal with.
The other thing is, there are just a lot of different kinds
of information that are in EHR systems, and it's getting more and more.
For example, there are diagnosis information such as ICD code in there.
And their medication information, there's lab test.
And their clinical notes represented as free text.
And their medical images.
And more and more genomic data getting into the EHR system.
So all of those different type of information requires different kind of
techniques to process them, to analyze them.
For example,
for text data like clinical notes, you need natural language processing.
And for image, you need specialized medical image analysis tools.
For structured data like diagnosis medications,
you would use general data mining techniques.
So people have to be able to pick the right tools to deal with the right data.
>> In a moment, we're going to talk about some work you've done around
the early diagnosis of congestive heart failure.
Which is interesting, particularly within the context of this course,
because chronic disease has really been the exemplar I've used throughout.
But first, in that work, you've had to use that free text data.
And in fact, you've extracted from it, clinical features.
That can be used by a machine learning algorithms.
So how do you do that?
How does that work?
>> Right so first for people who are not so
familiar with clinical notes, it's very rich information.
A lot of subtlety and a lot of symptoms are represented in the notes.
Only in the notes, not in the structured data.
So, it's very important to process the clinical notes, to extract
those symptom information, the severity information that are hidden in the text.
So, that's the reason of why we need to process clinical notes.
And the way we did that is to use a softer pipeline for
dealing with test called UIMA, unstructure information
management architecture that's originally developed at IBM research and
later on becomes an open source project that many people are using that.
Including later on the Watson project.
So it's facilitate natural language processing development and you develop
a pipeline of extractors to go through the text, extracting the information
that you cares about like symptoms that related to heart failures so
we used heart failure signs and symptoms, and also the context.
You not only want to know the symptoms are present in the texts, but
also whether it's in the positive context,
meaning that the patient is confirmed to have the symptoms, or
it's in the negative context, the patient doesn't have the symptom.
So, you also need to know the context.
So, we did many different extractors and
to extract that information and also the context.
So, it's a lot of work.
>> So armed with that information and
the structured information, you've developed a machine monitoring algorithm
to diagnose congestive heart failure earlier.
I should mention to the students without a clinical background that
congestive heart failure is the single most expensive medical condition.
And that diagnosing and treating it early, as we discussed in the course,
is true of most chronic diseases,
we can hope to avoid the complications that are very expensive to treat.
But it's very subtle at first, there is no test like a blood glucose you can
take, or a hemoglobin A1C to say you've got congestive heart failure.
So clinicians often don't pick up on it as early as might be the case.
Your model does, can you tell us how you did that?
>> Right, so that's a project we started when I was at IBM Research
a few years ago in collaboration with Geisinger health systems.
So we would visit Geisinger and I mean, trying to learn about what are the most
important or challenging problems that we can tackle together,
using data-mining machine learning techniques.
And they identify heart failures, one.
And at times, there are already many groups are interested in heart failures.
Mostly in the post-diagnosis phases in terms of readmission,
hospitalization, readmission after heart failure's diagnosis.
But that groups in Geisinger, the focuc,
they wanted to look at pre-diagnosis, look at what are the signs and
symptoms that lead to diagnosis of heart failures.
Can we identify the patient of high risk of developing heart failures earlier?
So the goal is for early detection.
And we leverage The first thing we did is we want to see whether that's
even possible.
Are there relevant signal in the data that earlier than the diagnosis time?
And we look at the clinical notes and that's where we first developed
natural language processing techniques to extract that and
the findings from those notes are very shocking.
In fact, just defining from the clinical notes,
we're able to publish in the journal in cardiac failures.
And thus, are medical journals that clinicians write.
There are some exciting findings in terms of signs and
symptoms that in the electronic house records
two years prior to the diagnosis, and that's very surprising.
Many people don't believe those.
I mean% of patients have that sign and
symptom already in the clinical notes two years before the diagnosis.
So those make us believe we can do something.
Then the question is how do we really leverage that reach the HR information.
So, it would be the integrated data analysis tools to integrate diagnosis,
lab measure, and medication, and
also clinical notes together to view the very reach patient profiles.
Then score those patient profiles using advanced
machine learning techniques to be able to predict a model.
And that helped us to develop this very accurate predicting model that can
predict heart failures diagnosis six to 12 months before the actual diagnosis.